A first year graduate course on theory and applications of random processes. Review of probability theory. Random sequences. Autocorrelation and covariance. Ergodicity and stationarity. Stochastic calculus. Poisson process. Gaussian process. Brownian motion/Wiener process. Markov process. Shot noise process. Linear systems driven by stochastic inputs. Elementary queueing theory.
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| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 3.57 | 1 sec · 20 öğr |
| 2024-2025 Fall | 3.44 | 1 sec · 38 öğr |
| 2023-2024 Fall | 3.07 | 1 sec · 13 öğr |
| 2022-2023 Fall | 3.50 | 1 sec · 35 öğr |
| 2021-2022 Fall | 3.41 | 1 sec · 22 öğr |
| 2020-2021 Fall | 3.32 | 1 sec · 24 öğr |
| 2019-2020 Fall | 3.40 | 1 sec · 16 öğr |
| 2018-2019 Fall | 3.01 | 1 sec · 45 öğr |
| 2017-2018 Fall | 3.24 | 1 sec · 22 öğr |
| 2016-2017 Fall | 3.20 | 1 sec · 38 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
To attend the midterm exam and get a grade >=30.
review of probability, random variables random variables (continued) Multiple random variables, transformations, characteristic functions, averages Multiple random variables, transformations, characteristic functions, averages Gaussian random vectors, sequences of random variables sequences of random variables, convergence, random processes (introduction) Random processes Random processes (continued) Random walks, other random process examples Poisson processes Markov chains Markov processes Selected topics, e.g., martingales, estimation, hypothesis testing Selected topics, e.g., martingales, estimation, hypothesis testing ECTS - Workload Table: Activities Number Hours Workload Homework 10 5 50 Preparation for Final exam 1 25 25 Midterm exam 2 2 4 Course hours 14 3 42 Final exam 1 3 3 Preparation for Midterm exam 2 15 30 Total Workload: 154 Total Workload / 30: 154 / 30 5.13 ECTS Credits of the Course: 5 Type of Course: Lecture Course Material: PC Teaching Methods: Lecture - Assignment